201116318 六、發明說明： 【發明所屬之技術領域】 本發明係有關於一種揮擊動作之肌能狀態分析系統、 方法及其電腦程式產品，特別是有關於一種利用肌電訊號 與揮擊速度資料以分析使用者揮擊動作是否正確的揮擊動 作之肌能狀態分析系統、方法及其電腦程式產品。 【先前技術】 鲁 先剞技術中’利用肌電訊號以分析使用者動作的肌肉 反應與施力力道，已常見於醫療技術領域，包括病患的肢 體復健訓練，義肢的使用訓練、癱瘓者的身體全衡訓練等。 亦如，體内器官的肌肉運作偵測，如心臟肌肉反應偵測， 肺部與胸膛肌肉的反應偵測，皆得以結合肌電訊號偵測技 術’但肌電訊號技術甚少應用於就運動領域方面。 事實上，選手進行運動訓練時，需相當重視各相關肌 肉是否正確施力，同時要避免過度的訓練以造成肌肉傷害 ® 的情形。以揮擊運動為例，揮擊運動係指需要多處肌肉瞬 間動作，講求肌肉爆發力'協調性的一系列動作。目前技 術多僅做單純的肌能分析資料，無法即時顯示揮擊動作所 使用到多處之肌肉群的施力狀態，故無法直接用於輔助改 善揮擊動作。 【發明内容】 本發明欲解決的問題係提供一種揮擊動作之肌能狀態 201116318 分析系統、方法及其電腦程式產品，用以分析使用者揮擊 運動的肌肉效能，更進一步時並可給予施力建議，以令使 用者校正其揮擊姿勢與動作。 明係揭露一種揮擊動作之肌能狀態分析系統，其 包括.一揮擊器具’用以供-使用者進行揮擊動作，其包 括-^迷度感’，用以當該揮擊器具被揮動時，感測該 揮具之加逮度以產生—揮擊速m複數個訊號债 貝j核:且 '以感應該使用者之複數個肌肉產生之複數個肌 貝料庠’用以儲存至少—肌能樣本值，每一肌 少包括一揮擊速度樣本值及其對應複數個肌肉 u樣本值，-肌能分析模組，用以 該揮擊速度資料.取得該複數個肌肉效能值;以=; :料=該揮擊速度資料和該複數個肌肉致能值與 果資料。—肌能樣本值進行㈣，以產生〜比對結 包括本::#揭露一種揮擊動作之肌能狀態分析方法，其 =值庫，儲存至少一肌能樣本值，每一肌能 能樣本H—揮擊速度樣本似其賴魏個肌肉效 、.二由—加速度感測器，感測一使用者使 揮 1 咖亍揮擊動作時該揮擊器具之加速度，以產;一： 速度貝料’取得該使用者之複數個肌肉產生 r號；分析該等肌電訊號與該揮擊速度資料“=複 固肌肉越值’·以及依據轉擊速度資料和該複數個肌 201116318 肉效能值與該資料庫中至少一肌能樣本值進行比對，以產 生一比對結果資料。 本發明之特點係在於本發明適用於且揮擊動作之運動 領域，即時分析出使用者之肌肉使用狀態與肌肉效能，提 供一比對結杲資料。更進一步時，可經由分析比對的結果 資料給予使用者適當的建議進而改善整體揮擊效率。其 次，本發明直接偵測使用者運動時，肌肉牵生的肌電訊號， 以分析出使用者之肌肉使用狀態與肌肉效能，再經由比對 模組，標準的肌能樣本值做比對，分析出哪一部分肌肉群 需要修正施力，以給予最適當的施力建議，藉此減少運動 傷害及提高訓練效率。 【實施方式】 茲配合圖式將本發明較佳實施例詳細說明如下。 首先請參照圖1A所繪示本發明實施例之揮擊動作之 肌能狀態分析系統架構示意圖，與圖1B與圖1C所繪示本 • 發明實施例之揮擊動作之肌能狀態分析系統方塊示意圖， 此系統包括一揮擊器具1、複數個訊號偵測模組2、一資料 庫33、一肌能分析模組31與一比對模組32。 揮擊器具1用以供一使用者進行揮擊動作。揮擊器具 1包括一加速度感測器11，其可以是多維度加速度計，如 二維度加速度計或三維度加速度計。本實施例之揮擊器具 1以球棒作說明，但不以此為限，只要是用以進行揮擊動 作的器具，如高爾夫球桿、網球拍、羽球拍等亦可。 201116318 本實施例中，加速度感測器11可配署 以::用者揮動球棒時’感測球棒被：末:產用 —揮擊迷度資料。 X M產生 使用2仙模組2以感應貼片作說明，感應貼片被貼在 吏用者身上，用以感應使用者在作揮擊動作時 在 的肌電訊號’各肌肉包括肩膀部位、手臂部： :::腰部位、手腕部位、大腿部位、小腿部位、腳掌 模=種以上的相關肌肉。在其他實施例中，訊號偵測 复斟處可歧市售可得之肌電訊縣置、設備或系統及 一對應的訊號感應元件，例如可擷取、量測與分析表面肌 電訊號，無線肌電訊號量測(EMG)設備及其微型化無線探 針藍芽八頻肌電訊號儀及其表面型電極、EMG肌電訊號 =測儀及其表面電極片等。更甚者’根據揮擊運動的肌肉 而求’訊號彳貞測模組2被配置於右半身或左半身的相關肌 肉部位。揮擊速度資料43與肌電訊號會被傳送至肌能分析 杈組31。加速度感測器u、訊號偵測模組2與肌能分析模 組31連接的模式如下： (1) 在加速度感測器11、訊號偵測模組2與肌能分 析模組31分別包括一無線通訊模組以進行無線通訊，以將 揮擊速度資料和各肌電訊號從加速度感測器n和訊號偵 測模組2傳送到肌能分析模組31。 (2) 將資料庫33 '肌能分析模組31和比對模組％ 同設置於一計算器3中，計算器3經由一有線或無線通訊 201116318 網路與加速度感測器u和各訊號偵測模組 • 於卜 哭仃通訊，例 如計鼻器3制—聰通訊模組並經由無線通_路與各 訊號偵測模組2進行通訊，以取得揮擊速度資料和各個肌 電訊號。如圖1A與圖1C，加速度感測器U、訊號偵測模 組2電性辆接至一無線通訊模組21，由無線通訊模組u 與計算器3無線連接，以傳輸揮擊速度資料和各個肌電訊 號至肌能分析模組31，其中計算器3係為個人電腦、伺服201116318 VI. Description of the Invention: [Technical Field] The present invention relates to a muscle energy state analysis system, method and computer program product for a swing action, and more particularly to a use of a myoelectric signal and a swing speed data A muscle energy state analysis system, method, and computer program product for analyzing whether a user's swipe action is correct or not. [Prior Art] In the Lu Xianyi technology, the use of myoelectric signals to analyze the muscle response and force of the user's movements has been common in the field of medical technology, including limb rehabilitation training for patients, training in the use of prosthetics, and the latter. The body is fully balanced and so on. For example, the detection of muscle function in internal organs, such as the detection of cardiac muscle response, the detection of lung and chest muscles, can be combined with the detection of myoelectric signal technology, but the myoelectric signal technology is rarely used for exercise. Domain aspect. In fact, when athletes exercise, they need to pay considerable attention to whether the relevant muscles are properly applied and avoid excessive training to cause muscle damage. Taking the swinging movement as an example, the swinging movement refers to a series of movements that require multiple muscles to move in an instant and emphasize the coordination of muscles. At present, the technology only uses simple muscle energy analysis data, and cannot immediately display the force state of the muscle groups used in the swing action, so it cannot be directly used to assist the improvement of the swing action. SUMMARY OF THE INVENTION The problem to be solved by the present invention is to provide a muscle energy state 201116318 analysis method, method and computer program product thereof for analyzing the muscle performance of a user's swinging motion, and further giving Force is recommended to allow the user to correct their swing postures and movements. The system discloses a muscle energy state analysis system for a swing action, which includes a swing device 'for a user to perform a swipe action, which includes a -^ sensation' for when the swing device is waved At the time, the swipe is sensed to generate a swipe speed m of a plurality of signals, and a plurality of muscles are generated by sensing the plurality of muscles of the user to store at least - a muscle energy sample value, each muscle comprising a stroke speed sample value and a corresponding plurality of muscle u sample values, a muscle energy analysis module for using the swing speed data to obtain the plurality of muscle performance values; =; : material = the swing speed data and the plurality of muscle enable values and fruit data. - Muscle energy sample values are performed (4) to generate ~ comparison knots including:: #Exposure a muscle energy state analysis method of a swipe action, which = value library, stores at least one muscle energy sample value, each muscle energy sample The H-swing speed sample is similar to the Wei muscle effect, and the second is the acceleration sensor, which senses the acceleration of the swinging device when a user makes a wave of slaps. The batting material 'obtains the muscles of the user to generate the r number; analyzes the electromyography signals and the swing speed data "=reinforces the muscles over the value" and according to the speed of the shifting speed data and the plurality of muscles 201116318 meat efficiency The value is compared with at least one muscle energy sample value in the database to generate a comparison result data. The invention is characterized in that the invention is applicable to the field of motion of the swipe action, and instantly analyzes the muscle use of the user. The state and muscle performance provide a comparison of the scarring data. Further, the user can appropriately improve the overall swing efficiency by analyzing the comparison result data. Secondly, the present invention directly detects the user's movement. At the time, the muscle-induced myoelectric signal is used to analyze the user's muscle use state and muscle performance, and then compare the standard muscle energy sample values through the comparison module, and analyze which part of the muscle group needs to be corrected. In order to give the most appropriate force recommendation, thereby reducing the sports injury and improving the training efficiency. [Embodiment] The preferred embodiment of the present invention will be described in detail below with reference to the drawings. First, please refer to FIG. 1A for the implementation of the present invention. The schematic diagram of the structure of the muscle energy state analysis system of the swing action, and the block diagram of the muscle energy state analysis system of the swing action of the embodiment of the present invention, which is shown in FIG. 1B and FIG. 1C, the system includes a swing device 1 The signal detecting module 2, a data library 33, a muscle energy analyzing module 31 and a matching module 32. The swinging device 1 is used for a user to perform a swinging action. The swinging device 1 includes an acceleration The sensor 11 can be a multi-dimensional accelerometer, such as a two-dimensional accelerometer or a three-dimensional accelerometer. The swinging device 1 of the embodiment is described by a bat, but not limited thereto, as long as it is used For the swinging action, such as a golf club, a tennis racket, a badminton racket, etc. 201116318 In this embodiment, the acceleration sensor 11 can be equipped with:: sensing the bat when the user swings the bat :: End: Production - Swinging fascination data. XM generation uses 2 sen module 2 to indicate the patch, and the sensor patch is attached to the user to sense the user's swinging action. In the myoelectric signal, each muscle includes the shoulder part, the arm part: ::: the waist part, the wrist part, the thigh part, the calf part, the sole of the foot = more than the relevant muscles. In other embodiments, the signal detection is recuperative A commercially available muscle and telecommunications county, device or system and a corresponding signal sensing component, such as measuring, measuring and analyzing surface myoelectric signals, wireless myoelectric signal measurement (EMG) devices and their Miniaturized wireless probe Bluetooth eight-frequency myoelectric signal meter and its surface electrode, EMG myoelectric signal = measuring instrument and its surface electrode sheet. Furthermore, the signal detection module 2 is disposed on the muscle portion of the right or left body. The swipe speed data 43 and the myoelectric signal are transmitted to the muscle energy analysis group 31. The modes of connecting the acceleration sensor u, the signal detecting module 2 and the muscle energy analyzing module 31 are as follows: (1) The acceleration sensor 11, the signal detecting module 2 and the muscle energy analyzing module 31 respectively include one The wireless communication module performs wireless communication to transmit the swing speed data and the myoelectric signals from the acceleration sensor n and the signal detection module 2 to the muscle energy analysis module 31. (2) The database 33' muscle energy analysis module 31 and the comparison module % are set in a calculator 3, and the calculator 3 communicates through a wired or wireless communication 201116318 network and acceleration sensor u and each signal Detection module • 卜 仃 仃 communication, such as the genius 3 system - Cong communication module and communicate with each signal detection module 2 via wireless channel to obtain the sprint speed data and each myoelectric signal . 1A and FIG. 1C, the acceleration sensor U and the signal detection module 2 are electrically connected to a wireless communication module 21, and the wireless communication module u is wirelessly connected with the calculator 3 to transmit the swing speed data. And each myoelectric signal to the muscle energy analysis module 31, wherein the calculator 3 is a personal computer, servo
器與筆記型電腦之其中之任一。本實施例以第2種方式進 行說明。 I 資料庫33儲存有複數一個以上的肌能樣本值5，其為 代表人在最合乎標準揮擊的動作下，各肌肉的施力與肌能 數值。每一個肌能樣本值5包括一揮擊速度樣本值51與其 對應複數個肌肉效能樣本值52。 本實施例中，肌能分析模組31主要是分析所有的肌電 訊號與揮擊速度資料43，以取得複數個肌肉效能值44，並 將肌肉效能值44與揮擊速度資料43傳輸至比對模組32。 肌能分析模組31包括一訊號分析模組311與一肌能判定模 組312，訊號分析模組311用以對各肌電訊號進行一時域 分析，以取得複數個肌肉施力強度值41。時域分析公式如 下，但不以此為限。.時域分析公式： T+t (公式1) iEMG= \EMG{t)dtAny of the devices and notebooks. This embodiment will be described in the second mode. The I database 33 stores a plurality of muscle energy sample values of 5, which are the values of the force and muscle energy of each muscle under the action of the most standard swing. Each muscle energy sample value 5 includes a swipe speed sample value 51 and a corresponding plurality of muscle performance sample values 52. In this embodiment, the muscle energy analysis module 31 mainly analyzes all the myoelectric signals and the swing speed data 43 to obtain a plurality of muscle performance values 44, and transmits the muscle performance values 44 and the swing speed data 43 to the ratio. For module 32. The muscle energy analysis module 31 includes a signal analysis module 311 and a muscle energy determination module 312. The signal analysis module 311 performs a time domain analysis on each of the myoelectric signals to obtain a plurality of muscle force intensity values 41. The time domain analysis formula is as follows, but not limited to this. Time domain analysis formula: T+t (Formula 1) iEMG= \EMG{t)dt
T 其中為肌電訊號；ζ·五MG為肌肉放電量，在此指 201116318 一肌肉施力強度值41 ; 為五MG經快速傅立葉轉換形 成的頻譜值。每一個肌肉群所對應的肌肉效能值44為（肌 肉施力強度值41/各肌肉施力強度值41之總和）xlOO%。 "月參照圖2所緣示本發明實施例之肌肉效能比對示意 圖’叙设施力的肌肉群包括A肌肉群、B肌肉群、C肌肉 群與D肌肉群，肌肉施力強度值41分別為A=0.35、 Β=〇·25、C=〇.3〇與d=〇.1〇，各肌肉施力強度值41之總和 為1，則A肌肉群的肌肉效能值44為（〇 35/1)χ1〇〇%=35%， B肌肉群的肌肉效能值44為（0.25/1) xl00%=25%，C肌 肉群的肌肉效能值44為（〇 3〇/1) χ1〇〇%=3〇%，D肌肉群 的肌肉效能值44為（o.w ) χ1〇〇%=1〇〇/〇。 比對模組32取得揮擊速度資料43及其對應的肌肉效 月b值44後，會讀取資料庫33的肌能樣本值5，將揮擊速 度資料43每一個揮擊速度樣本值51比對，從中找出一目 標肌能樣本值，其包括_擊速麟本值51相近或相同於 揮擊速度㈣43。肌肉效能值與資料庫33儲存的各肌肉 效能樣本值52係分別對應複數個肌肉屬性其中之一，比對 模組32會將各肌肉效能值44與目標肌能樣本值包括的目 標效能樣本值相輯，產纽對結果資料。輯的方式為 將具有相同肌肉屬性的肌肉效能值44與目標效能樣本值 進行比對’如：肌肉屬性同為A肌肉群的肌肉效能值料 與目標效能樣本值相互比對，肌肉屬性同為B肌肉群的肌 肉效能值44與目標效能樣本值相互比對.·等，以此類推。 201116318 比對結果資料會由顯示模組34進行顯示，顯示的方式乃經 由數值、圖表、和圖式等其中之任一種方式，來表現出揮 擊速度資料與揮擊速度樣本值之差異，以及肌肉效能值與 肌肉效能樣本值之差異。 比對模組32會依據揮擊速度資料與揮擊速度樣本值 之差異及肌肉效能值與相對的肌肉效能樣本值之差異，以 判定各肌肉效能值44是否包括至少一異常肌肉效能值 I 44，並產生比對結果資料，比對模組再根據比對結果資料 以產生施力建議資料。就本實施例而言，如果比對結果資 料為判定有異常肌肉效能值44，比對模組32會產生施力 建議資料以建議使用者調整其肌肉施力模式。相反的，即 比對模組32即不動作，或是建議使用者保持現在的施力模 式。 舉例而言，當肌肉屬性為A肌肉群的目標效能樣本值 為25%，肌肉屬性同為A肌肉群的肌肉效能值44為35%， • 故肌肉效能值44比目標效能樣本值高出10%，比對模組 32判定使用者的A肌肉群施力過大為異常肌肉效能值 44，應降低其施力程度，C肌肉群與D肌肉群施力過小同 為異常肌肉效能值44，應略提升其施力程度。比對模組32 即產生一施力建議資料，以建議使用者減少A肌肉群的施 力，並略提升C肌肉群與D肌肉群的施力，而施力建議資 料透過顯示模組34所顯示，以供使用者參考。 此外，訊號分析模組311更對各肌電訊號進行一頻域 201116318 分析，以取得一疲勞指標值42，頻域分析時，先將肌電訊 號作一快速傅立葉轉換為頻譜後，導入下列的頻域分析公 式（不以此為限）以取得疲勞指標值42:T is the myoelectric signal; ζ·5 MG is the muscle discharge volume, here refers to 201116318 a muscle force intensity value 41; is the spectrum value formed by the fast FFT conversion of the five MG. The muscle performance value 44 corresponding to each muscle group is (the sum of the muscle strength value 41 / the sum of the muscle strength values 41) x 100%. "Monthly Referring to Figure 2, the muscle performance comparison diagram of the embodiment of the present invention is shown in the figure. The muscle group of the facility includes the A muscle group, the B muscle group, the C muscle group and the D muscle group, and the muscle strength value 41 is respectively For A=0.35, Β=〇·25, C=〇.3〇 and d=〇.1〇, the sum of the muscle strength values 41 is 1, and the muscle performance value of the A muscle group is 44 (〇35 /1)χ1〇〇%=35%, muscle strength value of B muscle group is 44 (0.25/1) xl00%=25%, muscle muscle value of C muscle group is 44 (〇3〇/1) χ1〇〇 %=3〇%, muscle strength value 44 of D muscle group is (ow) χ1〇〇%=1〇〇/〇. After the comparison module 32 obtains the swing speed data 43 and its corresponding muscle effect month b value 44, the muscle energy sample value 5 of the database 33 is read, and the swing speed data 43 is used for each swing speed sample value 51. In comparison, a target muscle energy sample value is found, which includes the _ spurt value 51 is similar or the same as the swing speed (four) 43. The muscle performance value and each muscle performance sample value 52 stored in the database 33 correspond to one of a plurality of muscle attributes, and the comparison module 32 sets each muscle performance value 44 and the target muscle energy sample value to include the target performance sample value. The album, the production of the results of the data. The method is to compare the muscle performance value 44 with the same muscle attribute with the target efficacy sample value. For example, the muscle attribute is the same as the muscle performance value of the A muscle group and the target performance sample value, and the muscle attribute is the same. The muscle strength value 44 of the B muscle group is compared with the target efficacy sample value. etc., and so on. 201116318 The comparison result data is displayed by the display module 34, and the display manner is expressed by any one of numerical values, graphs, and patterns to express the difference between the swipe speed data and the swipe speed sample value, and The difference between the muscle performance value and the muscle performance sample value. The comparison module 32 determines whether each muscle performance value 44 includes at least one abnormal muscle performance value I 44 based on the difference between the swing speed data and the swing speed sample value and the difference between the muscle performance value and the relative muscle performance sample value. And the comparison result data is generated, and the comparison module further generates the recommendation data according to the comparison result data. For the present embodiment, if the comparison result data is determined to have an abnormal muscle performance value of 44, the comparison module 32 generates a force suggestion information to advise the user to adjust his muscle application mode. Conversely, the comparison module 32 does not operate, or the user is advised to maintain the current force application mode. For example, when the muscle attribute is 2% of the target performance sample of the A muscle group, and the muscle attribute is the same as the muscle performance value 44 of the A muscle group, the muscle performance value 44 is higher than the target performance sample value by 10%. %, the comparison module 32 determines that the user's A muscle group is excessively applied to the abnormal muscle performance value 44, and the degree of exertion should be reduced. The C muscle group and the D muscle group are too small to apply the abnormal muscle performance value 44. Slightly increase the degree of exertion. The comparison module 32 generates a force recommendation information to suggest that the user reduce the force of the A muscle group and slightly increase the force applied to the C muscle group and the D muscle group, and the force recommendation information is transmitted through the display module 34. Display for user reference. In addition, the signal analysis module 311 performs a frequency domain 201116318 analysis on each myoelectric signal to obtain a fatigue index value of 42. In the frequency domain analysis, the EMG signal is first converted into a spectrum by a fast Fourier transform, and then introduced into the following The frequency domain analysis formula (not limited to this) is used to obtain the fatigue index value 42:
MfpSD{fW 二 lPSD{f)df = \]PSD{f)df {公式” 0 MDF Z 0 其中，ΜλΡ指中心頻率，在此指一疲勞指標值42。 MDF(Median Frequency):在頻域上計算出所積分的面積相 φ 同於總面積的一半時，此點表示出肌肉在此時具有頻率改 變；也是將原始訊號經傅利葉轉換(FFT)成頻譜，可以用來 代表肌肉疲勞的疲勞指標’當肌肉呈現疲勞狀態時，其肌 電訊號的中心頻率會往低頻處移動。 接著’比對模組32更包括由肌肉施力強度值41與疲 勞指標值42為單位所形成之一二維座標軸32卜其劃分為 複數個象限（Quadrant)。比對模組32分析肌肉施力強度 鲁值41與疲勞指標值42所形成之一落點ρι (χ=肌肉施力強 度值41’ y=疲勞指標值42)及其所在象限，判定使用者是 否處於疲勞狀態，當使用者處於疲勞狀態，比對模組32產 生一休息建議資訊。 月多照圖3所綠示本發明實施例之二維座標軸321的 象限及落點P1示意圖，此落點P1代表在連續時間下所量 測到的肌電訊號，其被套入時域分析與頻域分析時’肌肉 把力強度值41與疲勞指標值42隨時間的變化量，即斜 各象限代表意義如下： 201116318 第一象限Q1 (力量增長，Force increase):若z•五wg 值和MDF值隨時間變化之斜率同時為正，表示其肌肉隨著 時間的演進而處於力量增加狀態。 第一象限Q2 (肌肉適應強度，Adaptation ):若z•五 值斜率為負和ΜίλΡ值斜率為正，表示其肌肉隨著時間的演 進而處於對目前揮擊運動的施力強度逐漸適應。 第二象限Q3 (力量衰退，F〇rce decrease):若/五 •值和值隨時間變化之斜率同時為負，表示其肌肉隨著 時間的演進而處於力量衰退狀態。 第四象限Q4 ( Fatigue ):若履G值斜率為正和从咖 值斜率為負，表示其肌肉隨著時間的演進而處於疲勞狀態。 當比對杈組32會根據落點ρι所在象限判定使用者是 否處於疲勞狀態。當落點P1位於第四象限Q4時，即判定MfpSD{fW two lPSD{f)df = \]PSD{f)df {formula" 0 MDF Z 0 where ΜλΡ refers to the center frequency, which refers to a fatigue index value of 42. MDF (Median Frequency): in the frequency domain When the integrated area phase φ is calculated to be half of the total area, this point indicates that the muscle has a frequency change at this time; it is also the Fourier transform (FFT) into the spectrum, which can be used to represent the fatigue index of muscle fatigue. When the muscle is in a fatigue state, the center frequency of the myoelectric signal moves to the low frequency. Then, the comparison module 32 further includes a two-dimensional coordinate axis formed by the muscle force intensity value 41 and the fatigue index value 42. 32 is divided into a plurality of quadrants. The comparison module 32 analyzes the muscle exertion strength Lu value 41 and the fatigue index value 42 to form a drop point ρι (χ = muscle force strength value 41' y = fatigue The indicator value 42) and its quadrant determine whether the user is in a fatigue state, and when the user is in a fatigue state, the comparison module 32 generates a rest suggestion information. The multi-dimensional display of the embodiment of the present invention is two-dimensional. Quadrant and drop of coordinate axis 321 P1 diagram, this drop point P1 represents the measured myoelectric signal in continuous time, which is inserted into the time domain analysis and the frequency domain analysis, the amount of change in the muscle strength value 41 and the fatigue index value 42 over time, That is, the meanings of the oblique quadrants are as follows: 201116318 First quadrant Q1 (Force increase): If the slope of the z·5 wg value and the MDF value change with time is positive, it means that the muscle is in strength over time. Increase the state. First quadrant Q2 (Adaptive Adaptation Strength, Adaptation): If the slope of the z•five value is negative and the slope of the ΜίλΡ value is positive, it indicates that the muscles are gradually exerting strength on the current swinging motion as time progresses. Adaptation. Second quadrant Q3 (power decay, F〇rce decrease): If the slope of the value of /5 value and time changes simultaneously, it means that its muscles are in a state of strength decline with the evolution of time. Fourth quadrant Q4 (Fatigue): If the slope of the G value is positive and the slope of the slave value is negative, it means that the muscles are fatigued with the evolution of time. When the comparison group 32 is determined according to the quadrant of the falling point ρι Whether a state of fatigue. When Q4, drop point P1 is located in the fourth quadrant, i.e., it is determined
其為代表人在最合乎鮮揮擊的動作 口 J W干 <平罕的動作下，各It is the representative's action in the most savvy action, J W dry <
肌此樣本值5包括一揮擊速 L肉效能樣本值52。 201116318 經由一加速度感測器li，感測一使用者使用一揮擊μ 具1進行揮擊動作時揮擊器具1之加速度， ^ Μ屋生一揮擊 速度資料（步驟S120)。本實施例中，捏墼 诨拏器具1以球棒 料 作說明，但不以此為限，只要是用以進行揮擊動作的器具， 如高爾夫球桿 '網球拍、羽球拍等亦可。加速度感測器'u 為多維度加速度計，如二維度加速度計或三維度加°速度 計。加速度感測器11配置於球棒末端，用以感測使用者ς 動球棒時’感應球棒被揮動的速度，以產生—揮擊速产， 取得使用者之複數個肌肉產生之複數個肌電訊號（步 驟SU0)。訊號偵測模組2以感應貼片作說明，感應貼片 被貼在使用者身上，用以感應使用者在作揮擊動作日^ '，各 肌肉產生的肌電訊號。The muscle sample value 5 includes a swipe speed L meat performance sample value 52. 201116318, through an acceleration sensor li, senses the acceleration of the swinging device 1 when a user performs a swipe action using a swipe μ, and generates a swipe speed data (step S120). In the present embodiment, the kneading device 1 is described by a bat bar, but not limited thereto, as long as it is a device for performing a swipe action, such as a golf club 'tennis racket, badminton racket, and the like. The acceleration sensor 'u is a multi-dimensional accelerometer such as a two-dimensional accelerometer or a three-dimensional accelerometer. The acceleration sensor 11 is disposed at the end of the bat to sense the speed at which the sensor bat is swung when the user smashes the bat, to generate a slamming speed, and to obtain a plurality of muscles generated by the user. Myoelectric signal (step SU0). The signal detecting module 2 is described by the sensing patch, and the sensing patch is attached to the user to sense the myoelectric signal generated by each muscle in the user's swinging action day.
分析各肌電訊號與揮擊速度資料43以取得複數個肌 肉^能值44(步驟⑽）。揮擊速度資㈣與肌電訊號會 ?达至肌能分析模組3卜加速度感測器u、訊號债測模 組2與肌能分析模組31連接的模式如下： ⑴在加速度感測器n、訊號偵難組2與肌能] 、，'且31分別包括—無線通訊模組以進行無線通訊，以》 揮擊速度資料和各肌電訊號從加速度感測器u和訊號d 測模組2傳送到肌能分析模組31。 儿 (2)將資料庫33 同設置於一計算器3中 、肌能分析模組31和比對模組32 ，計算器3經由一有線或無線通訊 12 201116318 網路與加速度感測器11和各訊號偵測模組2進行通訊，例 如計算器3使用一 USB通訊模組並經由無線通訊網路與各 訊號偵測模組2進行通訊，以取得揮擊速度資料和各個肌 電訊號。如圖1A與圖1C，加速度感測器11、訊號偵測模 組2電性耦接至一無線通訊模組21，由無線通訊模組21 與計算器3無線連接，以傳輸揮擊速度資料和各個肌電訊 號至肌能分析模組31，其中計算器3係為個人電腦、伺服 器與筆記型電腦之其中之任一。本實施例以第2種方式進 鲁行說明。 請同時參照圖5繪示本發明實施例之步驟S14 0之細部 流程圖，步騾S140包括數個細部流程： 對各肌電訊號進行一時域分析以取得複數個肌肉施力 強度值41 (步驟S141)。肌能分析模組31包括一訊號分析 模組311與一肌能判定模組312,訊號分析模組311用以對 各肌電訊號進行一時域分析，以取得複數個肌肉施力強度 Φ值41。時域分析公式： T+t iEMG= \EMG{t)dt (公式 1)Each muscle electrical signal and swing speed data 43 are analyzed to obtain a plurality of muscle muscle energy values 44 (step (10)). The speed of the swing (4) and the myoelectric signal will reach the muscle energy analysis module 3, the acceleration sensor u, the signal debt measurement module 2 and the muscle energy analysis module 31 are connected as follows: (1) in the acceleration sensor n, signal detection group 2 and muscle energy],, 'and 31 respectively include - wireless communication module for wireless communication, to sway speed data and each myoelectric signal from acceleration sensor u and signal d Group 2 is transferred to the muscle energy analysis module 31. (2) The database 33 is set in a calculator 3, the muscle energy analysis module 31 and the comparison module 32, and the calculator 3 via a wired or wireless communication 12 201116318 network and acceleration sensor 11 and Each of the signal detecting modules 2 communicates. For example, the calculator 3 uses a USB communication module to communicate with each of the signal detecting modules 2 via the wireless communication network to obtain the swing speed data and the respective myoelectric signals. As shown in FIG. 1A and FIG. 1C, the acceleration sensor 11 and the signal detection module 2 are electrically coupled to a wireless communication module 21, and the wireless communication module 21 and the calculator 3 are wirelessly connected to transmit the swing speed data. And each of the myoelectric signals to the muscle energy analysis module 31, wherein the calculator 3 is any one of a personal computer, a server and a notebook computer. This embodiment is described in the second way. Referring to FIG. 5, a detailed flowchart of step S14 0 of the embodiment of the present invention is shown. Step S140 includes several detailed processes: performing a time domain analysis on each myoelectric signal to obtain a plurality of muscle force intensity values 41 (steps) S141). The muscle energy analysis module 31 includes a signal analysis module 311 and a muscle energy determination module 312. The signal analysis module 311 is configured to perform a time domain analysis on each muscle electrical signal to obtain a plurality of muscle force intensity Φ values 41. . Time domain analysis formula: T+t iEMG= \EMG{t)dt (Equation 1)
T 其中為肌電訊號；WMG為肌肉放電量，在此指 一肌肉施力強度值41 ; 為五MG經快速傅立葉轉換形 成的頻譜值。 利用各肌肉施力強度值41與揮擊速度資料43以計算 出各肌肉效能值44 (步驟S142)。每一個肌肉群所對應的 13 201116318 41/各肌肉施力強度值 肌肉效能值44為（肌肉施力強度值 41之總和）xlOO%。 利用揮擊速度資料43從久Ηί7南 中至少一肌ς 肌肉效能值44與資料庫33 中至夕肌此樣本值5進行比對， (步驟S15〇)。比對模組32取;產生:比對結果資料 應的肌肉效能值44後，會讀取資度資料43及其對 a貝取貝枓庠33的肌能樣本值5。T is the myoelectric signal; WMG is the muscle discharge volume, here refers to a muscle exertion intensity value 41; is the spectral value formed by the fast FFT conversion of the five MGs. Each muscle exerting strength value 41 and swing speed data 43 are used to calculate respective muscle performance values 44 (step S142). Corresponding to each muscle group 13 201116318 41 / muscle strength value 44 muscle performance value 44 (the sum of muscle strength value 41) x lOO%. The swing speed data 43 is used to compare at least one tendon muscle performance value 44 of the long-term 77-South with the sample value 5 of the mid-day muscle of the database 33 (step S15〇). The comparison module 32 takes; generates: comparing the muscle performance value 44 of the result data, and reading the capital data 43 and the muscle energy sample value 5 of the abe.
比對模組32會依據揮擊速度資料43及各肌肉效能值44， 與資料庫33 t所有崎樣本值5之揮觀度樣本值51及 其對應各㈣效能樣本值進行比對，從巾找出—目標肌能 樣本值’其包括的揮擊速度樣本值51相近<❹同於揮擊速 度資料43，比對模、组32再將揮擊速度資料43及各肌肉效 能值44與目標肌能樣本值比對^比對時，比對模組％將 相同肌肉屬性的肌肉效能值44與肌肉效能樣本值52進行 比對，以產生比對結果資料，比對結果資料包括揮擊速度 資料43與縣速度樣本值51之差異，以及各肌肉效能值 與各肌肉效能樣本值52之差異。之後，由顯賴組％顯 示比對結果資料，顯示模組34係經由數值、圖表、和圖式 等其中之任-種方式’來顯示揮擊速度資料與揮擊速度樣 本值之差異’及各肌肉效能值與各肌肉效能樣本值之差異。 請同時參照圖6繪示本發明實施例之步驟sl5〇之細部 流程圖，本實鉍例中，肌肉效能值44與各肌肉效能樣本值 52係分別對應複數個肌肉屬性其中之一，步驟sl5〇包括 複數個細部流私* · 201116318 • 將相__性之肌肉效録44與_效能樣本值 對，以產生輯結果倾（步驟Sl51)。比對模 =H將揮擊速度資料43和各肌肉效能值44與資料庫 值再母進行比對，以決定一目標肌能樣本 樣本:= 和各肌肉效能值44與目標肌能 效能值44*肌肉為將具有相同肌_的肌肉 •模％ 示模組34顯示比對結果資料，顯示 值、圖表、和圖式其中之任—種方式，來 揮濞逮度資料43與揮擊速度樣本值5丨 個肌肉效能值44與各個肌肉效能樣本值52^差差里。及各 之依據各肌肉效能值與各肌肉效能樣本值 施力建議資料 ）’亚根據比對結果資料，產生一 建κΐ判定存在異常肌肉效能值44，產生施力建議資料以 施力模式(步驟S153)建= (步驟⑽)，或是建議使用者保持現在的施力模式 圖，7緣示本發明實施例之疲勞狀態分析流程 肌電圖4以利於了解，其執行於取得各 虎後（即步驟8130後），疲勞狀態分析流程包括·· 1各肌電喊進行-頻域分析以取得-疲勞指標值4 2 15 E S] 201116318 (步驟S210)。訊號分析模組311更對各肌電訊號進行一 頻域分析，以取得一疲勞指標值42。頻域分析時’先將肌 電訊號作一快速傅立葉轉換為頻譜後，導入下列的頻域分 析公式·The comparison module 32 compares the swept speed data 43 and the muscle performance values 44 with the sample value 51 of all the sample values of the database 33 and the corresponding (4) performance sample values. Find out - the target muscle energy sample value's including the swipe speed sample value 51 is similar to the same as the swipe speed data 43, the comparison mode, the group 32 and the swipe speed data 43 and the muscle performance values 44 and When the target muscle energy sample value is compared, the comparison module % compares the muscle performance value 44 of the same muscle attribute with the muscle performance sample value 52 to generate the comparison result data, and the comparison result data includes a swipe. The difference between the speed data 43 and the county speed sample value 51, and the difference between each muscle performance value and each muscle performance sample value 52. Thereafter, the comparison result data is displayed by the display group %, and the display module 34 displays the difference between the swipe speed data and the swipe speed sample value by any of the numerical values, graphs, and patterns. The difference between each muscle performance value and each muscle performance sample value. Please refer to FIG. 6 to show a detailed flowchart of the step s15 of the embodiment of the present invention. In the embodiment, the muscle performance value 44 and each muscle performance sample value 52 correspond to one of the plurality of muscle attributes, step sl5. 〇 Include multiple details * · · 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉 肌肉The comparison model = H compares the swing speed data 43 and the muscle performance values 44 with the database value to determine a target muscle energy sample: = and each muscle performance value 44 and the target muscle energy efficiency value 44 * Muscles are the muscles that will have the same muscle _ modulo module 34 display comparison results data, display values, charts, and patterns of any of the ways to swing the catch data 43 and the swing speed sample The value of 5 muscle performance values 44 is in the difference between each muscle performance sample value 52^. And each of the muscle performance values and the muscle strength test sample value suggestion data) 'sub-based comparison of the results of the data, the production of a κ ΐ determination of abnormal muscle performance value 44, generate force suggestion data to force mode (step S153) Construction = (Step (10)), or suggest that the user maintain the current force pattern diagram, and the 7th edge shows the fatigue state analysis process of the embodiment of the present invention to facilitate the understanding of the electromyogram 4, which is performed after obtaining each tiger ( That is, after step 8130), the fatigue state analysis process includes: 1 each muscle power shouting-frequency domain analysis to obtain - fatigue index value 4 2 15 ES] 201116318 (step S210). The signal analysis module 311 performs a frequency domain analysis on each of the myoelectric signals to obtain a fatigue index value of 42. In the frequency domain analysis, after the EMG signal is converted into a spectrum by fast Fourier transform, the following frequency domain analysis formula is introduced.
MfpSD{fW = °\PSDif)df J^\PSDifW i 公 0 MDF 2 〇 其中，MDF指中心頻率，在此指一疲勞指標值42。 MDF(Median Frequency):在頻域上計算出所積分的面積相 同於總面積的一半時，此點表示出肌肉在此時具有頻率改 變，即原始訊號經傅利葉轉換(FFT)成蘋譜，其用來代表肌 肉疲勞的疲勞指標。當肌肉呈現疲勞狀態時，其肌電訊號 的中心頻率會往低頻處移動。 由肌肉施力強度值41與疲勞指標值42為單位形成一 二維座標軸321 (步驟S220)。二維座標轴321劃分為複數 個象限’每一象限代表不同的生理狀態。比對模組32會分 析肌肉施力強度值41與疲勞指標值42所形成之一落點P1 所在之一目標象限（步驟S230)。本實施例中，各象限代 表思義為.弟一象限Q1 (力量增長，Force increase):若 zTMG值和值隨時間變化之斜率同時為正，表示其肌 肉隨著時間的演進而處於力量增加狀態。第二象限q2 (肌 肉適應強度，Adaptation):若/似扣值斜率為負和細厂值 斜率為正，表示其肌肉隨著時間的演進而處於對目前揮擊 運動的施力強度逐漸適應。第三象限q3 (力量衰退，F〇rce [MfpSD{fW = °\PSDif)df J^\PSDifW i Male 0 MDF 2 〇 where MDF is the center frequency, which refers to a fatigue index value of 42. MDF (Median Frequency): When calculating the integrated area in the frequency domain is equal to half of the total area, this point indicates that the muscle has a frequency change at this time, that is, the original signal is converted into a flat spectrum by Fourier transform (FFT). To represent the fatigue index of muscle fatigue. When the muscles are fatigued, the center frequency of the myoelectric signal moves to the low frequency. A two-dimensional coordinate axis 321 is formed by the muscle force intensity value 41 and the fatigue index value 42 (step S220). The two-dimensional coordinate axis 321 is divided into a plurality of quadrants. Each quadrant represents a different physiological state. The comparison module 32 analyzes one of the target quadrants in which the muscle application strength value 41 and the fatigue index value 42 form one of the landing points P1 (step S230). In this embodiment, each quadrant represents a quadrant Q1 (Force increase): if the zTMG value and the value of the slope change with time are positive, indicating that the muscle is increasing in strength over time. status. The second quadrant q2 (Adaptation): If the slope of the/like depreciation is negative and the slope of the fine factory value is positive, it indicates that the muscles gradually adapt to the current applied force of the swinging motion as time progresses. Third quadrant q3 (power decline, F〇rce [
16 201116318 decrease ):若/EMG值和MDF值隨時間變化之斜率同時為 負，表示其肌肉隨著時間的演進而處於力量衰退狀態。第 四象限Q4 ( Fatigue ):若/五MG值斜率為正和ΜΖλΡ值斜率 為負，表示其肌肉隨著時間的演進而處於疲勞狀態。比對 模組32會根據落點Ρ1所在象限判定使用者是否處於疲勞 狀態（步驟S240)，本實施例中，當落點Ρ1在第四象限 Q4時，比對模組32即判定使用者已處於疲勞狀態。更甚 者，比對模組32能在落點Ρ1位於第三象限Q3時，即判 ® 定使用者已進入疲勞狀態。當比對模組32判定使用者處於 疲勞狀態時，產生一休息建議資訊（步驟S241 )供使用者 參考；反之，則返回步驟S230，比對模組32重新偵測落 點Ρ1所在象限。 综上所述，乃僅記載本發明為呈現解決問題所採用的 技術手段之實施方式或實施例而已，並非用來限定本發明 專利實施之範圍。即凡與本發明專利申請範圍文義相符， • 或依本發明專利範圍所做的均等變化與修飾，皆為本發明 專利範圍所涵蓋。 【圖式簡單說明】 圖1Α繪示本發明實施例之揮擊動作之肌能狀態分析系統 架構不意圖， 圖1B與圖1C繪示本發明實施例之揮擊動作之肌能狀態分 析系統方塊示意圖； 17 201116318 圖2繪示本發明實施例之肌肉效能比對示意圖； 圖3繪示本發明實施例之二維座標軸的象限及落點示意 圖； / 圖4繪示本發明實施例之揮擊動作之肌能狀態分析方法 之流程圖； 圖5繪示本發明實施例之步驟S140之細部流程圖； 圖6繪示本發明實施例之步驟S150之細部流程圖；以及 圖7繪示本發明實施例之疲勞狀態分析流程圖。 【主要元件符號說明】 1 揮擊器具 11 加速度感測器 2 訊號偵測模組 21 無線傳輸器 3 計算器 31 肌能分析模組 311 訊號分析模組 312 肌能判定模組 32 比對模組 321 二維座標軸 33 資料庫 34 顯示模組 41 肌肉施力強度值 42 疲勞指標值 18 201116318 43 揮擊速度資料 44 肌肉效能值 5 肌能樣本值 51 揮擊速度樣本值 52 肌肉效能樣本值 Qi 第一象限 Q2 第二象限 Q3 第三象限 Q4 第四象限 PI 落點 1916 201116318 decrease ): If the slope of the /EMG value and the MDF value change with time is negative, it means that its muscles are in a state of strength decline with the evolution of time. The fourth quadrant Q4 (Fatigue): If the slope of the / MG value is positive and the slope of the ΜΖλΡ value is negative, it indicates that the muscle is in a state of fatigue with the evolution of time. The comparison module 32 determines whether the user is in a fatigue state according to the quadrant of the drop point (1 (step S240). In this embodiment, when the drop point Ρ1 is in the fourth quadrant Q4, the comparison module 32 determines that the user has In a state of fatigue. Moreover, the matching module 32 can determine that the user has entered a fatigue state when the landing point 位于1 is in the third quadrant Q3. When the comparison module 32 determines that the user is in a fatigue state, a break suggestion information is generated (step S241) for the user to refer to; otherwise, the process returns to step S230, and the comparison module 32 re-detects the quadrant of the drop point Ρ1. In the above, it is merely described that the present invention is an embodiment or an embodiment of the technical means for solving the problem, and is not intended to limit the scope of the practice of the present invention. That is, the equivalent changes and modifications made to the scope of the patent application of the present invention are included in the scope of the invention. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1B is a schematic diagram showing the structure of a muscle energy state analysis system of a swing action according to an embodiment of the present invention, and FIG. 1B and FIG. 1C are diagrams showing a muscle energy state analysis system block of a swing action according to an embodiment of the present invention. FIG. 2 is a schematic diagram showing the comparison of the muscle performance of the embodiment of the present invention; FIG. 3 is a schematic diagram showing the quadrant and the falling point of the two-dimensional coordinate axis of the embodiment of the present invention; FIG. 4 is a schematic diagram of the swing of the embodiment of the present invention; FIG. 5 is a detailed flow chart of step S140 of the embodiment of the present invention; FIG. 6 is a detailed flow chart of step S150 of the embodiment of the present invention; and FIG. 7 is a flowchart of the present invention. Flow chart of fatigue state analysis of the embodiment. [Main component symbol description] 1 Swing device 11 Acceleration sensor 2 Signal detection module 21 Wireless transmitter 3 Calculator 31 Muscle energy analysis module 311 Signal analysis module 312 Muscle energy determination module 32 Comparison module 321 2D coordinate axis 33 Database 34 Display module 41 Muscle strength value 42 Fatigue index value 18 201116318 43 Swing speed data 44 Muscle performance value 5 Muscle energy sample value 51 Swing speed sample value 52 Muscle performance sample value Qi One quadrant Q2 second quadrant Q3 third quadrant Q4 fourth quadrant PI landing point 19